How to normalize all the matrices in a loop so that each row sums up to 1
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parag gupta
el 20 de Mzo. de 2019
Editada: Moritz Hesse
el 20 de Mzo. de 2019
N = 4
n = 2
A = cell(1,N);
for i = 1:N
A{i} = rand(n,n)
end
celldisp(A)
From above command I will get 4 matrices.How to normalize all the matrices( ie all 4 matrices) so that each row sums up to 1.
Thanks
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harsha001
el 20 de Mzo. de 2019
Editada: harsha001
el 20 de Mzo. de 2019
There are two parts to your question - (a) how to normalise each row of a matrix at once, and (b) how to do it independently for each matrix in a cell array.
(a)
use the dot notation to divide each row by the sum of that row
So for a matrix M,
M = M./sum(M,2); % sum acros the 2nd dimension (column) and do a row-wise division
will normalise each row to sum to 1.
If instead you want to normalise each column, simply:
M = M./sum(M,1);
(b) You can either use a for loop to do the same for each matrix A{jj} of the cell array
for jj=1:N
A{jj} = A{jj}./sum(A{jj},2);
end
A = arrayfun( @(jj) A{jj}./sum(A{jj},2), 1:N , 'UniformOutput', false );
where i use the array fun to loop over 1 to N, setting uniform output to false so my result is also a cell-array. Imagine it like:
output = arrayfun( @jj, func(something), loop over 1 to N, 'UniformOutput', false)
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Más respuestas (2)
Steven Lord
el 20 de Mzo. de 2019
The sum of the absolute values of the elements of a vector is the 1-norm. You can use the normalize function introduced in release R2018a to normalize each row of a matrix by the 1-norm.
A = rand(6);
B = normalize(A, 2, 'norm', 1);
shouldBeCloseTo1 = sum(B, 2)
You can use a for loop or arrayfun to apply normalize to each matrix in the cell array.
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Moritz Hesse
el 20 de Mzo. de 2019
Editada: Moritz Hesse
el 20 de Mzo. de 2019
If you have the deep learning toolbox installed, you can use normr to normalise matrix rows. You can access cell contents with curly brace notation
N = 4
n = 2
A = cell(1,N);
for i = 1:N
A{i} = rand(n,n)
end
celldisp(A)
% Loop through cells and normalise matrix rows
for i = 1:N
A{i} = normr(A{i})
end
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